论文标题
知情的提案蒙特卡洛
Informed Proposal Monte Carlo
论文作者
论文摘要
任何搜索或采样算法解决反问题解决方案都需要指导才能有效。许多算法收集并应用有关该问题的信息，并以这种方式进行了很多改进。但是，由于无需午餐定理的结果，我们唯一可以确保搜索和采样算法的性能更高的方法就是构建有关问题的尽可能多的信息。在马尔可夫链蒙特卡洛采样（MCMC）的特殊情况下，我们回顾了如何通过选择提案分配来完成的，并且在基于问题的近似物理模型的情况下，如何使这种添加有关问题的更多信息的方式特别有效。高维模型空间的高度非线性逆散射问题可说明通过这种方法提高效率。
Any search or sampling algorithm for solution of inverse problems needs guidance to be efficient. Many algorithms collect and apply information about the problem on the fly, and much improvement has been made in this way. However, as a consequence of the the No-Free-Lunch Theorem, the only way we can ensure a significantly better performance of search and sampling algorithms is to build in as much information about the problem as possible. In the special case of Markov Chain Monte Carlo sampling (MCMC) we review how this is done through the choice of proposal distribution, and we show how this way of adding more information about the problem can be made particularly efficient when based on an approximate physics model of the problem. A highly nonlinear inverse scattering problem with a high-dimensional model space serves as an illustration of the gain of efficiency through this approach.
